





import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    SwiftFormerConfig,
    SwiftFormerForImageClassification,
    ViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__lowercase	: Union[str, Any]       =logging.get_logger(__name__)

__lowercase	: List[Any]       =torch.device("""cpu""")




def      a__			(       ):






   '''simple docstring'''






   UpperCAmelCase_           ="http://images.cocodataset.org/val2017/000000039769.jpg"
   UpperCAmelCase_           =Image.open(requests.get(lowercase__	,     stream=lowercase__	).raw	)
   return im




def      a__			(       lowercase__	):






   '''simple docstring'''






   if swiftformer_name == "swiftformer_xs":
      return torch.tensor([-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0, 8.8_6_8_5E-0_1, 2.4_3_6_0E-0_1]	)

   elif swiftformer_name == "swiftformer_s":
      return torch.tensor([3.9_6_3_6E-0_1, 2.3_4_7_8E-0_1, -1.6_9_6_3E0_0, -1.7_3_8_1E0_0, -8.6_3_3_7E-0_1]	)

   elif swiftformer_name == "swiftformer_l1":
      return torch.tensor([-4.2_7_6_8E-0_1, -4.7_4_2_9E-0_1, -1.0_8_9_7E0_0, -1.0_2_4_8E0_0, 3.5_5_2_3E-0_2]	)

   elif swiftformer_name == "swiftformer_l3":
      return torch.tensor([-2.5_3_3_0E-0_1, 2.4_2_1_1E-0_1, -6.0_1_8_5E-0_1, -8.2_7_8_9E-0_1, -6.0_4_4_6E-0_2]	)




def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =dct.pop(lowercase__	)
   UpperCAmelCase_           =val




def      a__			(       lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =[]
   for k in state_dict.keys():
      UpperCAmelCase_           =k
      if ".pwconv" in k:
         UpperCAmelCase_           =k_new.replace(".pwconv"	,     ".point_wise_conv"	)
      if ".dwconv" in k:
         UpperCAmelCase_           =k_new.replace(".dwconv"	,     ".depth_wise_conv"	)
      if ".Proj." in k:
         UpperCAmelCase_           =k_new.replace(".Proj."	,     ".proj."	)
      if "patch_embed" in k_new:
         UpperCAmelCase_           =k_new.replace("patch_embed"	,     "swiftformer.patch_embed.patch_embedding"	)
      if "network" in k_new:
         UpperCAmelCase_           =k_new.split("."	)
         if ls[2].isdigit():
            UpperCAmelCase_           ="swiftformer.encoder.network." + ls[1] + ".blocks." + ls[2] + "." + ".".join(ls[3:]	)
         else:
            UpperCAmelCase_           =k_new.replace("network"	,     "swiftformer.encoder.network"	)
      rename_keys.append((k, k_new)	)
   return rename_keys




@torch.no_grad()
def      a__			(       lowercase__	,     lowercase__	,     lowercase__	):






   '''simple docstring'''






   UpperCAmelCase_           =SwiftFormerConfig()

   # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
   UpperCAmelCase_           =1_0_0_0
   UpperCAmelCase_           ="huggingface/label-files"
   UpperCAmelCase_           ="imagenet-1k-id2label.json"
   UpperCAmelCase_           =json.load(open(hf_hub_download(lowercase__	,     lowercase__	,     repo_type="dataset"	)	,     "r"	)	)
   UpperCAmelCase_           ={int(lowercase__	): v for k, v in idalabel.items()}
   UpperCAmelCase_           =idalabel
   UpperCAmelCase_           ={v: k for k, v in idalabel.items()}

   # size of the architecture
   if swiftformer_name == "swiftformer_xs":
      UpperCAmelCase_           =[3, 3, 6, 4]
      UpperCAmelCase_           =[4_8, 5_6, 1_1_2, 2_2_0]

   elif swiftformer_name == "swiftformer_s":
      UpperCAmelCase_           =[3, 3, 9, 6]
      UpperCAmelCase_           =[4_8, 6_4, 1_6_8, 2_2_4]

   elif swiftformer_name == "swiftformer_l1":
      UpperCAmelCase_           =[4, 3, 1_0, 5]
      UpperCAmelCase_           =[4_8, 9_6, 1_9_2, 3_8_4]

   elif swiftformer_name == "swiftformer_l3":
      UpperCAmelCase_           =[4, 4, 1_2, 6]
      UpperCAmelCase_           =[6_4, 1_2_8, 3_2_0, 5_1_2]

   # load state_dict of original model, remove and rename some keys
   if original_ckpt:
      if original_ckpt.startswith("https"	):
         UpperCAmelCase_           =torch.hub.load_state_dict_from_url(lowercase__	,     map_location="cpu"	,     check_hash=lowercase__	)
      else:
         UpperCAmelCase_           =torch.load(lowercase__	,     map_location="cpu"	)
   UpperCAmelCase_           =checkpoint

   UpperCAmelCase_           =create_rename_keys(lowercase__	)
   for rename_key_src, rename_key_dest in rename_keys:
      rename_key(lowercase__	,     lowercase__	,     lowercase__	)

   # load HuggingFace model
   UpperCAmelCase_           =SwiftFormerForImageClassification(lowercase__	).eval()
   hf_model.load_state_dict(lowercase__	)

   # prepare test inputs
   UpperCAmelCase_           =prepare_img()
   UpperCAmelCase_           =ViTImageProcessor.from_pretrained("preprocessor_config"	)
   UpperCAmelCase_           =processor(images=lowercase__	,     return_tensors="pt"	)

   # compare outputs from both models
   UpperCAmelCase_           =get_expected_output(lowercase__	)
   UpperCAmelCase_           =hf_model(inputs["pixel_values"]	).logits

   assert hf_logits.shape == torch.Size([1, 1_0_0_0]	)
   assert torch.allclose(hf_logits[0, 0:5]	,     lowercase__	,     atol=1E-3	)

   Path(lowercase__	).mkdir(exist_ok=lowercase__	)
   print(F'Saving model {swiftformer_name} to {pytorch_dump_folder_path}'	)
   hf_model.save_pretrained(lowercase__	)


if __name__ == "__main__":
     __lowercase	: int       =argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--swiftformer_name""",
         default="""swiftformer_xs""",
         choices=["""swiftformer_xs""", """swiftformer_s""", """swiftformer_l1""", """swiftformer_l3"""],
         type=str,
         help="""Name of the SwiftFormer model you'd like to convert.""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""./converted_outputs/""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument("""--original_ckpt""", default=None, type=str, help="""Path to the original model checkpoint.""")

     __lowercase	: List[Any]       =parser.parse_args()
     convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)








import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




